225 research outputs found
Role of Rip2 in development of tumor-infiltrating MDSCs and bladder cancer metastasis.
Tumor invasion and metastases represent a complex series of molecular events that portends a poor prognosis. The contribution of inflammatory pathways mediating this process is not well understood. Nod-like receptors (NLRs) of innate immunity function as intracellular sensors of pathogen motifs and danger molecules. We propose a role of NLRs in tumor surveillance and in programming tumor-infiltrating lymphocytes (TILs). In this study, we examined the downstream serine/threonine and tyrosine kinase Rip2 in a murine model of bladder cancer. In Rip2-deficient C57Bl6 mice, larger orthotopic MB49 tumors developed with more numerous and higher incidence of metastases compared to wild-type controls. As such, increased tumor infiltration of CD11b+ Gr1hi myeloid-derived suppressor cells (MDSCs) with concomitant decrease in T cells and NK cells were observed in Rip2-deficient tumor bearing animals using orthotopic and subcutaneous tumor models. Rip2-deficient tumors showed enhanced epithelial-to-mesenchymal transition, with elevated expression of zeb1, zeb2, twist, and snail in the tumor microenvironment. We found that the absence of Rip2 plays an intrinsic role in fostering the development of granulocytic MDSCs by an autocrine and paracrine effect of granulocytic colony stimulating factor (G-CSF) expression. Our findings suggest that NLR pathways may be a novel modality to program TILs and influence tumor metastases
Cultivation of environmental values for university students under strategic background of environmental informationization
AbstractThe strategy of environmental informationization is promulgated for environmental protection under the background of information development in new period. At the present, the cultivation of environmental values for university students should be closely combined with the strategy of environmental informationization. Moreover, the environmental value of harmonious coexistence between human and nature should be created and developed. Starting from the strategic development of environmental informationization and the present environmental values of university students, the authors investigated the significant and measures of both environmental informationization promotion and the environmental values cultivation of university students.© 2011 Published by Elsevier Ltd. Selection and peer-review under responsibility of RIUD
Bayesian Optimization with Clustering and Rollback for CNN Auto Pruning
Pruning is an effective technique for convolutional neural networks (CNNs)
model compression, but it is difficult to find the optimal pruning policy due
to the large design space. To improve the usability of pruning, many auto
pruning methods have been developed. Recently, Bayesian optimization (BO) has
been considered to be a competitive algorithm for auto pruning due to its solid
theoretical foundation and high sampling efficiency. However, BO suffers from
the curse of dimensionality. The performance of BO deteriorates when pruning
deep CNNs, since the dimension of the design spaces increase. We propose a
novel clustering algorithm that reduces the dimension of the design space to
speed up the searching process. Subsequently, a rollback algorithm is proposed
to recover the high-dimensional design space so that higher pruning accuracy
can be obtained. We validate our proposed method on ResNet, MobileNetV1, and
MobileNetV2 models. Experiments show that the proposed method significantly
improves the convergence rate of BO when pruning deep CNNs with no increase in
running time. The source code is available at
https://github.com/fanhanwei/BOCR.Comment: Accepted by ECCV 202
Impact of vaccination on the COVID-19 pandemic in U.S. states
Governments worldwide are implementing mass vaccination programs in an effort to end the novel coronavirus (COVID-19) pandemic. Here, we evaluated the effectiveness of the COVID-19 vaccination program in its early stage and predicted the path to herd immunity in the U.S. By early March 2021, we estimated that vaccination reduced the total number of new cases by 4.4 million (from 33.0 to 28.6 million), prevented approximately 0.12 million hospitalizations (from 0.89 to 0.78 million), and decreased the population infection rate by 1.34 percentage points (from 10.10 to 8.76%). We built a Susceptible-Infected-Recovered (SIR) model with vaccination to predict herd immunity, following the trends from the early-stage vaccination program. Herd immunity could be achieved earlier with a faster vaccination pace, lower vaccine hesitancy, and higher vaccine effectiveness. The Delta variant has substantially postponed the predicted herd immunity date, through a combination of reduced vaccine effectiveness, lowered recovery rate, and increased infection and death rates. These findings improve our understanding of the COVID-19 vaccination and can inform future public health policies
Endogenous cross-region human mobility and pandemics
We study infectious diseases using a Susceptible-Infected-Recovered-Deceased model with endogenous cross-region human mobility. Individuals weigh the risk of infection against economic opportunities when moving across regions. The model predicts that the mobility rate of susceptible individuals declines with a higher infection rate at the destination. With cross-region mobility, a decrease in the transmission rate or an increase in the removal rate of the virus in any region reduces the global basic reproduction number (R0). Global R0 falls between the minimum and maximum of local R0s. A new method of Normalized Hat Algebra is developed to solve the model dynamics. Simulations indicate that a decrease in global R0 does not always imply a lower cumulative infection rate. Local and central governments may prefer different mobility control policies
A Survey on Visual Mamba
State space models (SSM) with selection mechanisms and hardware-aware architectures, namely Mamba, have recently shown significant potential in long-sequence modeling. Since the complexity of transformers’ self-attention mechanism is quadratic with image size, as well as increasing computational demands, researchers are currently exploring how to adapt Mamba for computer vision tasks. This paper is the first comprehensive survey that aims to provide an in-depth analysis of Mamba models within the domain of computer vision. It begins by exploring the foundational concepts contributing to Mamba’s success, including the SSM framework, selection mechanisms, and hardware-aware design. Then, we review these vision Mamba models by categorizing them into foundational models and those enhanced with techniques including convolution, recurrence, and attention to improve their sophistication. Furthermore, we investigate the widespread applications of Mamba in vision tasks, which include their use as a backbone in various levels of vision processing. This encompasses general visual tasks, medical visual tasks (e.g., 2D/3D segmentation, classification, image registration, etc.), and remote sensing visual tasks. In particular, we introduce general visual tasks from two levels: high/mid-level vision (e.g., object detection, segmentation, video classification, etc.) and low-level vision (e.g., image super-resolution, image restoration, visual generation, etc.). We hope this endeavor will spark additional interest within the community to address current challenges and further apply Mamba models in computer vision
A Stochastic Online Forecast-and-Optimize Framework for Real-Time Energy Dispatch in Virtual Power Plants under Uncertainty
Aggregating distributed energy resources in power systems significantly
increases uncertainties, in particular caused by the fluctuation of renewable
energy generation. This issue has driven the necessity of widely exploiting
advanced predictive control techniques under uncertainty to ensure long-term
economics and decarbonization. In this paper, we propose a real-time
uncertainty-aware energy dispatch framework, which is composed of two key
elements: (i) A hybrid forecast-and-optimize sequential task, integrating deep
learning-based forecasting and stochastic optimization, where these two stages
are connected by the uncertainty estimation at multiple temporal resolutions;
(ii) An efficient online data augmentation scheme, jointly involving model
pre-training and online fine-tuning stages. In this way, the proposed framework
is capable to rapidly adapt to the real-time data distribution, as well as to
target on uncertainties caused by data drift, model discrepancy and environment
perturbations in the control process, and finally to realize an optimal and
robust dispatch solution. The proposed framework won the championship in
CityLearn Challenge 2022, which provided an influential opportunity to
investigate the potential of AI application in the energy domain. In addition,
comprehensive experiments are conducted to interpret its effectiveness in the
real-life scenario of smart building energy management.Comment: Preprint. Accepted by CIKM 2
Towards Good Practices in Evaluating Transfer Adversarial Attacks
Transfer adversarial attacks raise critical security concerns in real-world,
black-box scenarios. However, the actual progress of this field is difficult to
assess due to two common limitations in existing evaluations. First, different
methods are often not systematically and fairly evaluated in a one-to-one
comparison. Second, only transferability is evaluated but another key attack
property, stealthiness, is largely overlooked. In this work, we design good
practices to address these limitations, and we present the first comprehensive
evaluation of transfer attacks, covering 23 representative attacks against 9
defenses on ImageNet. In particular, we propose to categorize existing attacks
into five categories, which enables our systematic category-wise analyses.
These analyses lead to new findings that even challenge existing knowledge and
also help determine the optimal attack hyperparameters for our attack-wise
comprehensive evaluation. We also pay particular attention to stealthiness, by
adopting diverse imperceptibility metrics and looking into new, finer-grained
characteristics. Overall, our new insights into transferability and
stealthiness lead to actionable good practices for future evaluations.Comment: An extended version can be found at arXiv:2310.11850. Code and a list
of categorized attacks are available at
https://github.com/ZhengyuZhao/TransferAttackEva
Retinal Fundus Image Enhancement Using the Normalized Convolution and Noise Removing
Retinal fundus image plays an important role in the diagnosis of retinal related diseases. The detailed information of the retinal fundus image such as small vessels, microaneurysms, and exudates may be in low contrast, and retinal image enhancement usually gives help to analyze diseases related to retinal fundus image. Current image enhancement methods may lead to artificial boundaries, abrupt changes in color levels, and the loss of image detail. In order to avoid these side effects, a new retinal fundus image enhancement method is proposed. First, the original retinal fundus image was processed by the normalized convolution algorithm with a domain transform to obtain an image with the basic information of the background. Then, the image with the basic information of the background was fused with the original retinal fundus image to obtain an enhanced fundus image. Lastly, the fused image was denoised by a two-stage denoising method including the fourth order PDEs and the relaxed median filter. The retinal image databases, including the DRIVE database, the STARE database, and the DIARETDB1 database, were used to evaluate image enhancement effects. The results show that the method can enhance the retinal fundus image prominently. And, different from some other fundus image enhancement methods, the proposed method can directly enhance color images
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